"""Reusable test doubles and settings factories."""

from __future__ import annotations

import asyncio

from atlasbot.config import Settings


class FakeLLM:
    """Deterministic LLM double for pipeline tests.

    Why:
    - keeps the answer engine tests fast and predictable.

    Input/Output:
    - accepts the same `chat()` signature as the real client;
    - returns canned JSON or text snippets based on the prompt content.
    """

    def __init__(self) -> None:
        self.calls: list[str] = []

    async def chat(self, messages, *, model=None, timeout_sec=None):
        """Return a prompt-shaped response and remember the last user prompt."""

        prompt = messages[-1]["content"]
        self.calls.append(prompt)
        if "normalized" in prompt and "keywords" in prompt:
            return '{"normalized":"What is Atlas?","keywords":["atlas"]}'
        if "needs_snapshot" in prompt:
            return '{"needs_snapshot": true, "answer_style": "direct"}'
        if "sub-questions" in prompt:
            return '[{"id":"q1","question":"What is Atlas?","priority":1}]'
        if "sub-question" in prompt:
            return "Atlas has 22 nodes."
        if "Answer using only the Fact Sheet" in prompt:
            return "Atlas has 22 nodes."
        if "final response" in prompt:
            return "Atlas has 22 nodes."
        if "Score response quality" in prompt:
            return '{"confidence":80,"relevance":90,"satisfaction":85,"hallucination_risk":"low"}'
        if "claims list" in prompt:
            return '{"claims": []}'
        return "{}"


class SlowFakeLLM(FakeLLM):
    """Variant that sleeps briefly so timeout guards can be exercised."""

    async def chat(self, messages, *, model=None, timeout_sec=None):
        """Delay before answering to make budget handling deterministic."""

        await asyncio.sleep(0.02)
        return await super().chat(messages, model=model, timeout_sec=timeout_sec)


def build_test_settings() -> Settings:
    """Create a fully populated `Settings` instance for unit tests."""

    return Settings(
        matrix_base="",
        auth_base="",
        bot_user="",
        bot_pass="",
        room_alias="",
        server_name="",
        bot_mentions=(),
        matrix_bots=(),
        ollama_url="",
        ollama_model="base",
        ollama_model_fast="fast",
        ollama_model_smart="smart",
        ollama_model_genius="genius",
        ollama_fallback_model="",
        ollama_timeout_sec=1.0,
        ollama_retries=0,
        ollama_api_key="",
        http_port=8090,
        internal_token="",
        kb_dir="",
        vm_url="",
        ariadne_state_url="",
        ariadne_state_token="",
        snapshot_ttl_sec=30,
        thinking_interval_sec=30,
        quick_time_budget_sec=15.0,
        smart_time_budget_sec=45.0,
        genius_time_budget_sec=180.0,
        conversation_ttl_sec=300,
        snapshot_pin_enabled=False,
        queue_enabled=False,
        nats_url="",
        nats_stream="",
        nats_subject="",
        nats_result_bucket="",
        fast_max_angles=1,
        smart_max_angles=1,
        genius_max_angles=1,
        fast_max_candidates=1,
        smart_max_candidates=1,
        genius_max_candidates=1,
        fast_llm_calls_max=9,
        smart_llm_calls_max=17,
        genius_llm_calls_max=32,
        llm_limit_multiplier=1.5,
        state_db_path="/tmp/atlasbot_test_state.db",
    )